practical
Occurrence data
Explore occurrence records
occ.sterechinus <- read.csv("data/occurrences_sterechinus.csv", header=T, sep=";")
head(occ.sterechinus)## decimalLatitude decimalLongitude
## 1 -77.85000 166.6667
## 2 -77.83333 166.5667
## 3 -77.83330 166.5000
## 4 -77.83330 166.5500
## 5 -77.67000 -35.5000
## 6 -77.66667 -35.5000ggplot(occ.sterechinus, aes(x=decimalLongitude, y=decimalLatitude)) +
geom_point(stat="identity", colour="blue", alpha=0.5)
Environmental layers
depth <- raster("data/environmental_layers/depth.nc")
geomorphology <- raster("data/environmental_layers/geomorphology.nc")
ice_cover_min <- raster("data/environmental_layers/ice_cover_min.nc")
ice_cover_max <- raster("data/environmental_layers/ice_cover_max.nc")
ice_thickness_min <- raster("data/environmental_layers/ice_thickness_min.nc")
ice_thickness_max <- raster("data/environmental_layers/ice_thickness_max.nc")
mixed_layer_depth <- raster("data/environmental_layers/mixed_layer_depth.nc")
POC_2005_2012_min <- raster("data/environmental_layers/POC_2005_2012_min.nc")
POC_2005_2012_max <- raster("data/environmental_layers/POC_2005_2012_max.nc")
roughness <- raster("data/environmental_layers/roughness.nc")
sediments <- raster("data/environmental_layers/sediments.nc")
seafloor_current_speed <- raster("data/environmental_layers/seafloor_current_speed.nc")
seafloor_sali_2005_2012_min <- raster("data/environmental_layers/seafloor_sali_2005_2012_min.nc")
seafloor_sali_2005_2012_max <- raster("data/environmental_layers/seafloor_sali_2005_2012_max.nc")
seafloor_temp_2005_2012_min <- raster("data/environmental_layers/seafloor_temp_2005_2012_min.nc")
seafloor_temp_2005_2012_max <- raster("data/environmental_layers/seafloor_temp_2005_2012_max.nc")
slope <- raster("data/environmental_layers/slope.nc")
predictors_stack <- stack(depth, geomorphology,ice_cover_min, ice_cover_max, ice_thickness_min, ice_thickness_max, mixed_layer_depth, POC_2005_2012_min, POC_2005_2012_max, roughness, sediments, seafloor_current_speed, seafloor_sali_2005_2012_max, seafloor_temp_2005_2012_max, slope)Pixel resolution of environmental layers
Convert pixel resolution of predictors_stack with raster::aggregate. Choices: 0.1, 1, 10?
# check current pixel resolution of predictors_stack
predictors_stack_5 <- aggregate(predictors_stack, fact=5)
predictors_stack_10 <- aggregate(predictors_stack, fact=10)
plot(predictors_stack)
plot(predictors_stack_5)
plot(predictors_stack_10)
# pass predictors_stack to run() function
source("scripts/run_yOur_SDM.R")
res_1 <- run(predictors_stack, "res_0.1")##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1240 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.9827
## tolerance is fixed at 0.001
## ntrees resid. dev.
## 50 0.687
## now adding trees...
## 100 0.5771
## 150 0.5199
## 200 0.4909
## 250 0.4768
## 300 0.468
## 350 0.4628
## 400 0.4592
## 450 0.4563
## 500 0.4565
## 550 0.4565
## 600 0.4557
## 650 0.4557
## 700 0.4568
## 750 0.4581
## 800 0.4606
## 850 0.4614
## 900 0.4632
## 950 0.4645
## 1000 0.4668
## 1050 0.4692
## 1100 0.4719
## 1150 0.4737## fitting final gbm model with a fixed number of 650 trees for id
##
## mean total deviance = 0.983
## mean residual deviance = 0.313
##
## estimated cv deviance = 0.456 ; se = 0.064
##
## training data correlation = 0.851
## cv correlation = 0.717 ; se = 0.029
##
## training data AUC score = 0.983
## cv AUC score = 0.949 ; se = 0.009
##
## elapsed time - 0.1 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1240 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.9827
## tolerance is fixed at 0.001
## ntrees resid. dev.
## 50 0.7
## now adding trees...
## 100 0.5955
## 150 0.5426
## 200 0.5137
## 250 0.4993
## 300 0.4913
## 350 0.4852
## 400 0.4806
## 450 0.4781
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## 950 0.4737
## 1000 0.4751
## 1050 0.4767
## 1100 0.478
## 1150 0.479
## 1200 0.4812
## 1250 0.4817## fitting final gbm model with a fixed number of 700 trees for id
##
## mean total deviance = 0.983
## mean residual deviance = 0.292
##
## estimated cv deviance = 0.471 ; se = 0.062
##
## training data correlation = 0.866
## cv correlation = 0.71 ; se = 0.024
##
## training data AUC score = 0.987
## cv AUC score = 0.945 ; se = 0.01
##
## elapsed time - 0.1 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1240 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.9827
## tolerance is fixed at 0.001
## ntrees resid. dev.
## 50 0.6971
## now adding trees...
## 100 0.5885
## 150 0.5365
## 200 0.5097
## 250 0.493
## 300 0.4839
## 350 0.4784
## 400 0.4755
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## 900 0.4761
## 950 0.4773
## 1000 0.4795
## 1050 0.4809
## 1100 0.4826
## 1150 0.4833
## 1200 0.4855## fitting final gbm model with a fixed number of 550 trees for id
##
## mean total deviance = 0.983
## mean residual deviance = 0.32
##
## estimated cv deviance = 0.471 ; se = 0.035
##
## training data correlation = 0.848
## cv correlation = 0.709 ; se = 0.014
##
## training data AUC score = 0.982
## cv AUC score = 0.944 ; se = 0.006
##
## elapsed time - 0.09 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1240 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.9827
## tolerance is fixed at 0.001
## ntrees resid. dev.
## 50 0.7376
## now adding trees...
## 100 0.6343
## 150 0.5842
## 200 0.5577
## 250 0.5444
## 300 0.5364
## 350 0.534
## 400 0.5324
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## 900 0.539
## 950 0.5407
## 1000 0.5436
## 1050 0.5456
## 1100 0.5478
## 1150 0.5496## fitting final gbm model with a fixed number of 500 trees for id
##
## mean total deviance = 0.983
## mean residual deviance = 0.357
##
## estimated cv deviance = 0.532 ; se = 0.033
##
## training data correlation = 0.825
## cv correlation = 0.667 ; se = 0.026
##
## training data AUC score = 0.977
## cv AUC score = 0.929 ; se = 0.007
##
## elapsed time - 0.09 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1240 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.9827
## tolerance is fixed at 0.001
## ntrees resid. dev.
## 50 0.7683
## now adding trees...
## 100 0.6434
## 150 0.584
## 200 0.5565
## 250 0.5454
## 300 0.5393
## 350 0.5354
## 400 0.5319
## 450 0.5301
## 500 0.5279
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## 950 0.5372
## 1000 0.5381
## 1050 0.539
## 1100 0.5411
## 1150 0.5435## fitting final gbm model with a fixed number of 600 trees for id
##
## mean total deviance = 0.983
## mean residual deviance = 0.324
##
## estimated cv deviance = 0.526 ; se = 0.024
##
## training data correlation = 0.844
## cv correlation = 0.724 ; se = 0.021
##
## training data AUC score = 0.982
## cv AUC score = 0.942 ; se = 0.004
##
## elapsed time - 0.09 minutes
res_5 <- run(predictors_stack_5, "res_0.5")##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1155 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.7886
## tolerance is fixed at 8e-04
## ntrees resid. dev.
## 50 0.5954
## now adding trees...
## 100 0.5271
## 150 0.5002
## 200 0.4895
## 250 0.4851
## 300 0.4825
## 350 0.4819
## 400 0.4836
## 450 0.4866
## 500 0.4891
## 550 0.4923
## 600 0.496
## 650 0.4991
## 700 0.5028
## 750 0.5074
## 800 0.5122
## 850 0.5167
## 900 0.5223
## 950 0.5256
## 1000 0.53## fitting final gbm model with a fixed number of 350 trees for id
##
## mean total deviance = 0.789
## mean residual deviance = 0.358
##
## estimated cv deviance = 0.482 ; se = 0.037
##
## training data correlation = 0.744
## cv correlation = 0.536 ; se = 0.058
##
## training data AUC score = 0.965
## cv AUC score = 0.914 ; se = 0.012
##
## elapsed time - 0.09 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1155 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.7886
## tolerance is fixed at 8e-04
## ntrees resid. dev.
## 50 0.5805
## now adding trees...
## 100 0.5099
## 150 0.4805
## 200 0.4652
## 250 0.4568
## 300 0.4515
## 350 0.448
## 400 0.4461
## 450 0.4445
## 500 0.4442
## 550 0.4451
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## 650 0.4454
## 700 0.4471
## 750 0.4482
## 800 0.4488
## 850 0.4496
## 900 0.4511
## 950 0.4526
## 1000 0.4531
## 1050 0.4549
## 1100 0.4561
## 1150 0.4588## fitting final gbm model with a fixed number of 500 trees for id
##
## mean total deviance = 0.789
## mean residual deviance = 0.307
##
## estimated cv deviance = 0.444 ; se = 0.067
##
## training data correlation = 0.795
## cv correlation = 0.631 ; se = 0.05
##
## training data AUC score = 0.976
## cv AUC score = 0.935 ; se = 0.014
##
## elapsed time - 0.1 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1155 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.7886
## tolerance is fixed at 8e-04
## ntrees resid. dev.
## 50 0.5923
## now adding trees...
## 100 0.5254
## 150 0.496
## 200 0.4825
## 250 0.4758
## 300 0.4718
## 350 0.4699
## 400 0.469
## 450 0.4686
## 500 0.4685
## 550 0.4704
## 600 0.4721
## 650 0.4725
## 700 0.4744
## 750 0.4778
## 800 0.4805
## 850 0.482
## 900 0.4839
## 950 0.4847
## 1000 0.4871
## 1050 0.4892## fitting final gbm model with a fixed number of 500 trees for id
##
## mean total deviance = 0.789
## mean residual deviance = 0.308
##
## estimated cv deviance = 0.468 ; se = 0.021
##
## training data correlation = 0.798
## cv correlation = 0.57 ; se = 0.039
##
## training data AUC score = 0.977
## cv AUC score = 0.915 ; se = 0.012
##
## elapsed time - 0.09 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1155 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.7886
## tolerance is fixed at 8e-04
## ntrees resid. dev.
## 50 0.6213
## now adding trees...
## 100 0.5581
## 150 0.533
## 200 0.5231
## 250 0.5197
## 300 0.5177
## 350 0.5164
## 400 0.5173
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## 550 0.518
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## 650 0.5219
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## 750 0.5261
## 800 0.5286
## 850 0.5314
## 900 0.5337
## 950 0.5369
## 1000 0.5395
## 1050 0.5421## fitting final gbm model with a fixed number of 350 trees for id
##
## mean total deviance = 0.789
## mean residual deviance = 0.371
##
## estimated cv deviance = 0.516 ; se = 0.028
##
## training data correlation = 0.739
## cv correlation = 0.53 ; se = 0.058
##
## training data AUC score = 0.964
## cv AUC score = 0.899 ; se = 0.016
##
## elapsed time - 0.09 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1155 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.7886
## tolerance is fixed at 8e-04
## ntrees resid. dev.
## 50 0.662
## now adding trees...
## 100 0.5906
## 150 0.562
## 200 0.551
## 250 0.5496
## 300 0.5479
## 350 0.5476
## 400 0.5506
## 450 0.5516
## 500 0.5555
## 550 0.5576
## 600 0.5596
## 650 0.5625
## 700 0.5646
## 750 0.5688
## 800 0.5731
## 850 0.5759
## 900 0.58
## 950 0.5833
## 1000 0.5864## fitting final gbm model with a fixed number of 350 trees for id
##
## mean total deviance = 0.789
## mean residual deviance = 0.373
##
## estimated cv deviance = 0.548 ; se = 0.075
##
## training data correlation = 0.733
## cv correlation = 0.537 ; se = 0.053
##
## training data AUC score = 0.963
## cv AUC score = 0.894 ; se = 0.019
##
## elapsed time - 0.08 minutes
res_10 <- run(predictors_stack_10, "res_1")##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1110 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.6462
## tolerance is fixed at 6e-04
## ntrees resid. dev.
## 50 0.5044
## now adding trees...
## 100 0.4554
## 150 0.4339
## 200 0.4263
## 250 0.4241
## 300 0.4224
## 350 0.4228
## 400 0.4229
## 450 0.4235
## 500 0.4252
## 550 0.427
## 600 0.4286
## 650 0.4315
## 700 0.4333
## 750 0.4352
## 800 0.4375
## 850 0.4402
## 900 0.4431
## 950 0.4465
## 1000 0.4486## fitting final gbm model with a fixed number of 300 trees for id
##
## mean total deviance = 0.646
## mean residual deviance = 0.337
##
## estimated cv deviance = 0.422 ; se = 0.043
##
## training data correlation = 0.675
## cv correlation = 0.518 ; se = 0.046
##
## training data AUC score = 0.959
## cv AUC score = 0.916 ; se = 0.021
##
## elapsed time - 0.08 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1110 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.6462
## tolerance is fixed at 6e-04
## ntrees resid. dev.
## 50 0.5139
## now adding trees...
## 100 0.475
## 150 0.4608
## 200 0.4557
## 250 0.4561
## 300 0.4581
## 350 0.4617
## 400 0.4649
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## 600 0.4828
## 650 0.4879
## 700 0.4936
## 750 0.4981
## 800 0.5028
## 850 0.5079
## 900 0.5125
## 950 0.5171
## 1000 0.522## fitting final gbm model with a fixed number of 200 trees for id
##
## mean total deviance = 0.646
## mean residual deviance = 0.364
##
## estimated cv deviance = 0.456 ; se = 0.04
##
## training data correlation = 0.656
## cv correlation = 0.442 ; se = 0.034
##
## training data AUC score = 0.951
## cv AUC score = 0.903 ; se = 0.011
##
## elapsed time - 0.07 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1110 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.6462
## tolerance is fixed at 6e-04
## ntrees resid. dev.
## 50 0.5054
## now adding trees...
## 100 0.466
## 150 0.449
## 200 0.442
## 250 0.4421
## 300 0.4429
## 350 0.445
## 400 0.4479
## 450 0.4488
## 500 0.4515
## 550 0.4551
## 600 0.4597
## 650 0.4626
## 700 0.4673
## 750 0.471
## 800 0.4746
## 850 0.4796
## 900 0.4841
## 950 0.4881
## 1000 0.4914## fitting final gbm model with a fixed number of 200 trees for id
##
## mean total deviance = 0.646
## mean residual deviance = 0.355
##
## estimated cv deviance = 0.442 ; se = 0.03
##
## training data correlation = 0.672
## cv correlation = 0.446 ; se = 0.028
##
## training data AUC score = 0.955
## cv AUC score = 0.897 ; se = 0.009
##
## elapsed time - 0.07 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1110 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.6462
## tolerance is fixed at 6e-04
## ntrees resid. dev.
## 50 0.517
## now adding trees...
## 100 0.4747
## 150 0.456
## 200 0.4489
## 250 0.4461
## 300 0.4451
## 350 0.4444
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## 650 0.4518
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## 800 0.4596
## 850 0.4614
## 900 0.4632
## 950 0.4658
## 1000 0.4686## fitting final gbm model with a fixed number of 350 trees for id
##
## mean total deviance = 0.646
## mean residual deviance = 0.333
##
## estimated cv deviance = 0.444 ; se = 0.028
##
## training data correlation = 0.69
## cv correlation = 0.453 ; se = 0.051
##
## training data AUC score = 0.958
## cv AUC score = 0.895 ; se = 0.013
##
## elapsed time - 0.07 minutes
##
##
## GBM STEP - version 2.9
##
## Performing cross-validation optimisation of a boosted regression tree model
## for id and using a family of bernoulli
## Using 1110 observations and 15 predictors
## loading user-supplied fold vector
## creating 4 initial models of 50 trees
##
## folds are stratified by prevalence
## total mean deviance = 0.6462
## tolerance is fixed at 6e-04
## ntrees resid. dev.
## 50 0.5426
## now adding trees...
## 100 0.5018
## 150 0.4887
## 200 0.4854
## 250 0.4858
## 300 0.4857
## 350 0.49
## 400 0.4949
## 450 0.5009
## 500 0.5077
## 550 0.5127
## 600 0.5175
## 650 0.5217
## 700 0.5273
## 750 0.533
## 800 0.5386
## 850 0.5446
## 900 0.5504
## 950 0.5565
## 1000 0.5628## fitting final gbm model with a fixed number of 200 trees for id
##
## mean total deviance = 0.646
## mean residual deviance = 0.369
##
## estimated cv deviance = 0.485 ; se = 0.062
##
## training data correlation = 0.639
## cv correlation = 0.416 ; se = 0.048
##
## training data AUC score = 0.949
## cv AUC score = 0.888 ; se = 0.013
##
## elapsed time - 0.07 minutes
res_1## [[1]]
## depth geomorphology ice_cover_min ice_cover_max ice_thickness_min
## CtM 17.930666 1.2366647 0.1303194 20.346240 0.05250484
## CtSD 6.307375 0.1522527 0.1194673 4.232842 0.09175831
## ice_thickness_max mixed_layer_depth POC_2005_2012_min
## CtM 10.203085 2.572348 5.8195153
## CtSD 2.444769 1.351595 0.9420132
## POC_2005_2012_max roughness sediments seafloor_current_speed
## CtM 24.750621 4.420418 2.7557811 3.6960056
## CtSD 7.205073 0.754408 0.2060642 0.8038036
## seafloor_sali_2005_2012_max seafloor_temp_2005_2012_max slope
## CtM 0.8421448 2.7325400 2.511146
## CtSD 0.3432618 0.9939546 1.023322
##
## [[2]]
## [,1] [,2] [,3] [,4] [,5]
## AUC 0.9558000 0.9330000 0.9361000 0.9722000 0.9305000
## COR 0.6992987 0.6636518 0.7071519 0.7978194 0.6885880
## TSS 0.6833179 0.5692363 0.5963489 0.7602871 0.5137931
## maxSSS 0.2857768 0.4591752 0.6050939 0.3014095 0.4262716
## valid_test_data 88.4146341 86.6421569 87.7862595 90.6976744 84.9315068
## prop_test 9.5833333 70.4166667 12.0833333 7.9166667 12.5000000
## [,6] [,7] [,8] [,9] [,10]
## AUC 0.9713000 0.9498000 0.9273000 0.9305000 0.9453000
## COR 0.7442441 0.7540633 0.6528057 0.6812191 0.7282217
## TSS 0.8524668 0.5913978 0.5729266 0.6677165 0.5612270
## maxSSS 0.4044831 0.6222833 0.3543146 0.4056526 0.4633763
## valid_test_data 91.4893617 87.1794872 86.7224880 89.5966030 86.4321608
## prop_test 7.0833333 10.0000000 70.4166667 37.5000000 37.9166667
## [,11] [,12] [,13] [,14] [,15]
## AUC 0.9427000 0.9592000 0.9362000 0.9396000 0.9085000
## COR 0.6898372 0.7377930 0.6842144 0.7190538 0.5963976
## TSS 0.6148148 0.7703037 0.6436905 0.5942961 0.4773810
## maxSSS 0.5319660 0.3623791 0.3334083 0.4928258 0.4408957
## valid_test_data 87.6543210 91.3875598 88.4297521 87.5930521 83.8028169
## prop_test 11.2500000 13.3333333 35.0000000 38.3333333 12.5000000
## [,16] [,17] [,18] [,19] [,20]
## AUC 0.9335000 0.9407000 0.9524000 0.9374000 0.9379000
## COR 0.6688055 0.7049988 0.7851446 0.6896559 0.7171650
## TSS 0.6799601 0.6382372 0.5873016 0.6559345 0.6397569
## maxSSS 0.3999113 0.4908271 0.5040245 0.5137912 0.4746118
## valid_test_data 89.0995261 88.7254902 86.6666667 89.0394089 89.0243902
## prop_test 14.1666667 15.8333333 7.5000000 61.6666667 15.0000000
##
## [[3]]
## class : RasterLayer
## dimensions : 350, 3600, 1260000 (nrow, ncol, ncell)
## resolution : 0.1, 0.1 (x, y)
## extent : -180, 180, -80, -45 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
## source : memory
## names : layer
## values : 0.008006955, 0.9354443 (min, max)res_5## [[1]]
## depth geomorphology ice_cover_min ice_cover_max ice_thickness_min
## CtM 18.325566 3.453612 0.6106815 20.225266 0.06408695
## CtSD 6.225785 1.678995 0.7345645 4.123862 0.13361150
## ice_thickness_max mixed_layer_depth POC_2005_2012_min
## CtM 8.704239 2.254451 12.617786
## CtSD 3.688243 1.091642 3.486626
## POC_2005_2012_max roughness sediments seafloor_current_speed
## CtM 12.374563 4.348182 1.5513819 3.73427
## CtSD 3.471007 1.072554 0.9120895 1.34220
## seafloor_sali_2005_2012_max seafloor_temp_2005_2012_max slope
## CtM 2.601947 6.004284 3.129683
## CtSD 1.022258 1.398749 1.079657
##
## [[2]]
## [,1] [,2] [,3] [,4] [,5]
## AUC 0.9147000 0.8910000 0.9465000 0.9019000 0.9507000
## COR 0.4195777 0.4951637 0.6939180 0.5336381 0.7064927
## TSS 0.5277934 0.3744476 0.4687637 0.4207921 0.6971596
## maxSSS 0.2407876 0.3214172 0.4952894 0.3874982 0.3901202
## valid_test_data 88.6666667 85.3562005 87.6923077 86.3247863 93.1034483
## prop_test 8.3870968 67.7419355 13.5483871 10.3225806 12.2580645
## [,6] [,7] [,8] [,9] [,10]
## AUC 0.9619000 0.9277000 0.8998000 0.9329000 0.9339000
## COR 0.6865479 0.6425200 0.4877488 0.5997191 0.6495390
## TSS 0.7467532 0.4641944 0.4361249 0.5259248 0.4834078
## maxSSS 0.2323910 0.4399640 0.3115352 0.2924519 0.4462696
## valid_test_data 91.8699187 87.1559633 86.8894602 89.0214797 87.9795396
## prop_test 7.0967742 10.9677419 69.6774194 36.1290323 39.3548387
## [,11] [,12] [,13] [,14] [,15]
## AUC 0.8832000 0.9083000 0.8998000 0.9148000 0.9281000
## COR 0.4640875 0.5647255 0.5133615 0.6379993 0.5943235
## TSS 0.4775414 0.4656627 0.4470613 0.4840809 0.3903509
## maxSSS 0.4087196 0.3088708 0.4539722 0.3527552 0.3412696
## valid_test_data 88.0503145 87.6344086 87.2685185 87.8787879 85.6115108
## prop_test 11.6129032 12.9032258 31.6129032 41.9354839 12.2580645
## [,16] [,17] [,18] [,19] [,20]
## AUC 0.8536000 0.8943000 0.8602000 0.8749000 0.9486000
## COR 0.3723716 0.4479509 0.5439102 0.4720726 0.6831350
## TSS 0.2612267 0.4030303 0.1987578 0.4118551 0.5507246
## maxSSS 0.2923341 0.3693720 0.4358779 0.2610224 0.4163966
## valid_test_data 83.5106383 86.7403315 76.6666667 86.3213811 89.4409938
## prop_test 14.1935484 10.3225806 9.0322581 65.8064516 14.8387097
##
## [[3]]
## class : RasterLayer
## dimensions : 70, 720, 50400 (nrow, ncol, ncell)
## resolution : 0.5, 0.5 (x, y)
## extent : -180, 180, -80, -45 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
## source : memory
## names : layer
## values : 0.01162865, 0.8395866 (min, max)res_10## [[1]]
## depth geomorphology ice_cover_min ice_cover_max ice_thickness_min
## CtM 10.941070 3.713193 0.07883493 25.475356 0
## CtSD 1.494837 1.387697 0.11893146 4.468935 0
## ice_thickness_max mixed_layer_depth POC_2005_2012_min
## CtM 3.160188 1.863450 17.612380
## CtSD 1.111991 1.375602 4.086493
## POC_2005_2012_max roughness sediments seafloor_current_speed
## CtM 12.137185 4.46075 1.3268326 3.1796923
## CtSD 5.388202 2.61066 0.3380201 0.4709964
## seafloor_sali_2005_2012_max seafloor_temp_2005_2012_max slope
## CtM 3.349486 9.487713 3.2138700
## CtSD 1.645406 3.023571 0.9886124
##
## [[2]]
## [,1] [,2] [,3] [,4] [,5]
## AUC 0.9386000 0.8529000 0.9253000 0.9457000 0.9046000
## COR 0.4557411 0.4215006 0.5922593 0.6030513 0.4283150
## TSS 0.6065163 0.3460458 0.3586674 0.4203013 0.2641026
## maxSSS 0.2785036 0.3585402 0.3317644 0.3075857 0.2542343
## valid_test_data 92.2535211 88.5989011 87.6923077 90.0000000 86.8965517
## prop_test 8.1818182 64.5454545 15.4545455 11.8181818 13.6363636
## [,6] [,7] [,8] [,9] [,10]
## AUC 0.9229000 0.9121000 0.8712000 0.9145000 0.9027000
## COR 0.4827105 0.5038063 0.3521728 0.5276575 0.4322445
## TSS 0.4345794 0.3996004 0.3014568 0.4305223 0.2834986
## maxSSS 0.3366517 0.2759588 0.2693344 0.3157113 0.2388737
## valid_test_data 90.4347826 89.2156863 87.5668449 89.7959184 86.5979381
## prop_test 7.2727273 10.0000000 69.0909091 35.4545455 41.8181818
## [,11] [,12] [,13] [,14] [,15]
## AUC 0.9000000 0.8712000 0.8778000 0.8944000 0.8737000
## COR 0.4026478 0.4220008 0.4342483 0.4170871 0.3607531
## TSS 0.3408046 0.3927885 0.2574526 0.2392720 0.2842713
## maxSSS 0.2509619 0.3140128 0.3291845 0.2922217 0.3315728
## valid_test_data 88.5350318 89.5953757 87.1604938 85.7506361 87.5912409
## prop_test 10.9090909 11.8181818 32.7272727 40.9090909 10.0000000
## [,16] [,17] [,18] [,19] [,20]
## AUC 0.9324000 0.9250000 0.8878000 0.8698000 0.8704000
## COR 0.5983217 0.4258726 0.5478889 0.3401614 0.3484633
## TSS 0.4490446 0.5500000 0.1437908 0.1730864 0.1997636
## maxSSS 0.3057400 0.3435409 0.3888977 0.2649450 0.3153624
## valid_test_data 90.2857143 91.0714286 81.6666667 85.0622407 84.9056604
## prop_test 16.3636364 7.2727273 8.1818182 68.1818182 16.3636364
##
## [[3]]
## class : RasterLayer
## dimensions : 35, 360, 12600 (nrow, ncol, ncell)
## resolution : 1, 1 (x, y)
## extent : -180, 180, -80, -45 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
## source : memory
## names : layer
## values : 0.01888601, 0.6893824 (min, max)